Centralized truck platooning matching with compliance considerations

As an emerging mode of trucking operations, platooning refers to virtual coupling of multiple trucks in a convoy, which brings energy and driver labor savings. While centralized truck platooning is expected to yield the most benefit, the issue of truck compliance with a central matching plan has not received much attention despite its practical relevance. Assuming the presence of a platform for centralized truck platooning matching, this study tackles the problem of maximizing total truck utility gain from platooning while minimizing the probability of truck noncompliance with the matching plan. Considering trucks with different origins and destinations, multiple routing possibilities, time window constraints, and different positions in the formed platoon, we first propose a computational procedure to develop the truck platooning matching graph. Then, we characterize the truck investigation process of a matching plan and show the approximate equivalence between the minimum probability of truck noncompliance with a matching plan and the minimum number of blocking pairs. Two integer programming models are subsequently formulated, without and with the possibility of money transfer between trucks in a platoon. To enable compact model formulations, a series of theoretical investigations is conducted to reduce the matching graph by identifying and eliminating edges that will surely not appear in the optimal matching plan. For the model with money transfer which is more challenging to solve, an efficient Benders decomposition-based algorithm is proposed. We further extend the problem to allow a truck to platoon multiple times in its trip, which is plausible for long-haul trips. As the original matching graph is no longer sufficient for multiple-time platooning, a new algorithm is developed with the core idea of constructing connected and time-compatible subgraphs, based on which a modified integer programming model is formulated. Extensive numerical experiments are performed to validate the theoretical investigations, models, and algorithms, providing rich insights about centralized truck platooning matching with compliance considerations. These insights help inform truck platooning deployment toward greater system compliance and efficiency.